README.md

In opoyc/sftools: Automate Statistical Forecasting's workflows

Statistical Forecasting team's tools

Last version: sftools (0.1.5)

Installation:

Remote

From Github

remotes::install_github("opoyc/sftools") devtools::install_github("opoyc/sftools")

From Gitlab

install_sanofi_pkg <- function(){
  choice <- menu(choices = c("kinapi", "sftools", "None"), title = "Which package would you like to install?")
  if(choice == 1){
    pkg <- "https://emea-aws-gitlab.sanofi.com:3001/statistical_forecasting/packages/kinapi.git"
  } else if(choice == 2) {
    pkg <- "https://emea-aws-gitlab.sanofi.com:3001/statistical_forecasting/packages/sftools.git"
  } else if(choice == 3){
    message("Bye!")
  }
  cred <- git2r::cred_user_pass(rstudioapi::askForPassword("Username"), rstudioapi::askForPassword("Password"))
  devtools::install_git(pkg, credentials = cred)
}

Then run:

install_sanofi_pkg()

Objectives:

1. Having everything (possible) in one place
2. Less time running and fixing scripts and more time thinking and generating insights
3. Automate workflows/pipelines
4. Functions' authors will maintain and solve the issues, push the fixes and build the package's next version.
5. Have a uniform syntaxis to name objects and arguments and outputs.

Functions:

1. Statistical
2. Adjusted Coefficient of Variance sftools::acov()
3. Winsorization sftools::winsorize()
4. Outlier method sftools::outlier_method()
5. Seasonality detection sftools::seas_detect() (beta)
6. Generate fitted sftools::generate_fitted()
7. Recommend starting date sftools::tscut()
8. Summary
9. Filter the last 12 months sftools:::sum_twelve()
10. Generate segmentation classes sftools:::class_volval()
11. Load common databases
12. Wrangled/tidy version of GBU and Life Savers data bases sftools::load_db()
13. Kinaxis
14. sftools::read_knx() automatically detects KNX tables and performs tidy transformations. Current compability:
    • [Forecast Item] - ABC XYZ Calculation
    • [Forecast Item] Configuration (and life savings)
    • [Forecast Item] Level Definition
    • Active Regressor Summary
    • Causal Factor Cleansing - Summary
    • Edit Regressor Values
    • Forecast Comparison
    • Forecast Items
    • Regressor Usage Summary
    • Regressor Values
    • Regressors
    • Statistical Outliers Cleansing
    • Demand Waterfall

Known problems

The package have several depedencies, if you face a problem installing it please update them using:

suppressMessages(
  {
    packages = c('tidyverse', 'janitor', 'dplyr', 'readr', 'stringr', 'DescTools'
    , 'dvmisc', 'purrr', 'pracma', 'lubridate', 'tibble', 'tidyr', 'changepoint'
    , 'forecast', 'stlplus', 'readxl')
    check_packages <- lapply(
      packages,
      FUN = function(x) {
        if (!require(x, character.only = TRUE)) {
          install.packages(x, dependencies = TRUE)
          library(x, character.only = TRUE)
        }
      }
    )
    #source: https://vbaliga.github.io/verify-that-r-packages-are-installed-and-loaded/
  }
)

opoyc/sftools documentation built on July 31, 2020, 3:33 a.m.